Abstract
With the advancement of big data and education technology, MOOCs (Massive Online Open Courses) has become a popular education model in online education community. A large number of online courses with diverse disciplinary background are offered freely to global learners. When a learner finishes a series of courses, it is very important to effectively and efficiently recommend the most relevant courses to study next. Traditional item-based recommendation methods are all pointwise approaches where models bias towards estimating the precise rating or relevance score of each item. It would be better to model this problem from a pairwise learning perspective which is more close to the ranking nature of course recommendation. In this paper, we combine item-based collaborative filtering and Bayesian Personalized Ranking for course recommendation problem. We theoretically derive the optimization schema based on Bayesian Personalized Ranking and develop a novel neural network model, called Bayesian Personalized Ranking Network (BPRN), which learns pairwise course preference for each user given her historically enrolled courses. With extensive experiments on a large-scale MOOCs enrollment dataset from XuetangX, we empirically demonstrate that our BPRN framework performs better than state-of-the-art item-based course recommendation methods.
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Notes
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Sampling negative examples for implicit feedback dataset is a hot research topic. While the focus of this paper is to propose a new neural network for course recommendation, therefore we adopt a simple uniform sampling approach.
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In our pilot experiment, we find that sampling different negative course at each epoch can increase the variance of training set and reduce the risk of overfitting.
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The best \(\lambda \) for each method are \(10^{-5}\) for FISM, \(10^{-6}\) for NAIS and 0.001 for NAIS+BPRN.
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Acknowledgement
We would like to thank the anonymous reviewers for their helpful comments. This work is supported by the National Key Research and Development Program of China (2018YFB1004502) and the National Natural Science Foundation of China (61702532, 61532001).
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Li, X., Li, X., Tang, J., Wang, T., Zhang, Y., Chen, H. (2020). Improving Deep Item-Based Collaborative Filtering with Bayesian Personalized Ranking for MOOC Course Recommendation. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12274. Springer, Cham. https://doi.org/10.1007/978-3-030-55130-8_22
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